A preconditioned fast Hermite finite element method for space-fractional diffusion equations

Meng Zhao; Aijie Cheng; Hong Wang

doi:10.3934/dcdsb.2017178

Article Contents

2017, Volume 22, Issue 9: 3529-3545. Doi: 10.3934/dcdsb.2017178

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A preconditioned fast Hermite finite element method for space-fractional diffusion equations

1.
School of Mathematics, Shandong University, Jinan, Shandong 250100, China
2.
Department of Mathematics, University of South Carolina, Columbia, SC 29208, USA

* Corresponding author: Aijie Cheng
* Corresponding author: Aijie Cheng

Received: July 31, 2016

Revised: March 31, 2017

Published: October 2017

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Abstract

We develop a fast Hermite finite element method for a one-dimensional space-fractional diffusion equation, by proving that the stiffness matrix of the method can be expressed as a Toeplitz block matrix. Then a block circulant preconditioner is presented. Numerical results are presented to show the utility of the fast method.

Keywords:

Anomalous diffusion,
space-fractional diffusion equation,
hermite finite element,
block Toeplitz matrix,
fast Fourier transform,
preconditioned conjugate gradient squared method.

Mathematics Subject Classification: Primary:35R11, 65N30, 65F10.

Citation:

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Table 1. The condition number of stiffness matrix $\mathsf{A}$ with $K=1$, $\gamma=0.5$, $\beta=0.5$

h	$2^{-8} $	$2^{-9} $	$2^{-10} $
$Cond(\mathsf{A})$	$ 2.329917 \times 10^{6}$	$ 9.320396 \times 10^{6}$	$ 3.728232 \times 10^{7}$

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Table 2. The $L^2$ error and $H^{1-\frac{\beta}{2}}$ error of the cubic Hermite element method with $\beta=0.1,0.5,0.9$

$\beta$	h	DOF	$ \\|u_h - u \\|_{L^2}$	order	$ \\|u_h - u \\|_{H^{1-\frac{\beta}{2}}}$	order
0.5	$2^{-3} $	16	$ 4.872892 \times 10^{-4}$		$ 2.512934 \times 10^{-3}$
	$2^{-4} $	32	$ 3.573631 \times 10^{-5}$	3.7693	$ 2.576521 \times 10^{-4}$	3.2858
	$2^{-5} $	64	$ 2.236217 \times 10^{-6}$	3.9982	$ 2.481970 \times 10^{-5}$	3.3758
	$2^{-6} $	128	$ 1.342295 \times 10^{-7}$	4.0582	$ 2.249708 \times 10^{-6}$	3.4636
	$2^{-7} $	256	$ 7.909661 \times 10^{-9}$	4.0849	$ 2.033970 \times 10^{-7}$	3.4673
0.1	$2^{-3} $	16	$ 7.851432 \times 10^{-4}$		$ 8.011606 \times 10^{-3}$
	$2^{-4} $	32	$ 6.192597 \times 10^{-5}$	3.6643	$ 1.304843 \times 10^{-3}$	2.6352
	$2^{-5} $	64	$ 4.208159 \times 10^{-6}$	3.8792	$ 1.805192 \times 10^{-4}$	2.8536
	$2^{-6} $	128	$ 2.713900 \times 10^{-7}$	3.9547	$ 2.295487 \times 10^{-5}$	2.9752
	$2^{-7} $	256	$ 1.794745 \times 10^{-8}$	3.9185	$ 2.808095 \times 10^{-6}$	3.0311
0.9	$2^{-3} $	16	$ 3.622149 \times 10^{-4}$		$ 4.846137 \times 10^{-4}$
	$2^{-4} $	32	$ 2.767086 \times 10^{-5}$	3.7104	$ 4.110739 \times 10^{-5}$	3.5593
	$2^{-5} $	64	$ 1.826287 \times 10^{-6}$	3.9213	$ 3.012015 \times 10^{-6}$	3.7705
	$2^{-6} $	128	$ 1.180438 \times 10^{-7}$	3.9515	$ 2.132241 \times 10^{-7}$	3.8202
	$2^{-7} $	256	$ 7.376961 \times 10^{-9}$	4.0001	$ 1.415327 \times 10^{-8}$	3.9131

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Table 3. Performance of Gauss, CGS methods with $\beta=0.5$

	Gauss		CGS
h	CPU(s)	Itr. #	CPU(s)	Itr. #
$2^{-6}$	0.0916		0.2103	137
$2^{-7}$	0.4865		1.5369	272
$2^{-8}$	3.5858		9.8734	415
$2^{-9}$	24.4774		62.7682	665
$2^{-10}$	186.9874		391.5595	899
$2^{-11}$	1485.1450		2731.0751	1676
$2^{-12}$	out of memory		out of memory

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Table 4. Performance of FCGS, SFCGS and PFCGS methods with $\beta=0.5$

	FCGS		SFCGS		CFCGS
h	CPU(s)	Itr. #	CPU(s)	Itr. #	CPU(s)	Itr. #
$2^{-6}$	0.0832	137	0.0305	7	0.0341	9
$2^{-7}$	0.1809	272	0.0358	7	0.0419	10
$2^{-8}$	0.2890	415	0.0503	7	0.0445	11
$2^{-9}$	0.9907	665	0.0680	8	0.0653	12
$2^{-10}$	2.4525	899	0.0932	8	0.1046	14
$2^{-11}$	16.0752	1676	0.1536	9	0.2422	16
$2^{-12}$	26.4751	6421	0.1914	9	0.5955	19
$2^{-13}$	N/A	$>$ 30,000	0.6319	10	1.4107	22

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